Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems.
We propose an approach that recasts the task as automated knowledge base construction:
- Prompting an LLM to synthesize a 3NF-compliant relational schema from Wikipedia infobox timelines,
- Populating the schema to obtain a queryable database,
- Generating and executing SQL queries against it, with QA accuracy serving as an extrinsic evaluation of the constructed knowledge base.
In a controlled grid of three schema generators crossed with six query models, the schema source accounts for 79.5% of the exact match (EM) variance against 1.6% for the query model:
Replacing the schema, and the prompt scaffolding derived from it, shifts EM by 14.7 to 20.0 points, whereas replacing the query model under a fixed schema shifts it by 4.4 to 12.1.
From this evidence, we distill three candidate schema-design principles:
- Balanced normalization,
- Semantic naming,
- Consistent temporal anchoring,
framed as correlational hypotheses.
Our best configuration (Gemini 2.5 Flash schemas + Gemini-2.0-Flash queries) reaches 80.39 EM, 11.5 points above the strongest reported baseline (68.89 EM); an open-weights configuration reaches 79.52.